Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

Staff line removal is a crucial pre-processing step in Optical Music Recognition. In this paper we propose a novel approach for staff line removal, based on Generative Adversarial Networks. We convert staff line images into patches and feed them into a U-Net, used as Generator. The Generator intends to produce staff-less images at the output. Then the Discriminator does binary classification and differentiates between the generated fake staff-less image and real ground truth staff less image. For training, we use a Loss function which is a weighted combination of L2 loss and Adversarial loss. L2 loss minimizes the difference between real and fake staff-less image. Adversarial loss helps to retrieve more high quality textures in generated images. Thus our architecture supports solutions which are closer to ground truth and it reflects in our results. For evaluation we consider the ICDAR/GREC 2013 staff removal database. Our method achieves superior performance in comparison to other conventional approaches on the same dataset.

Staff line Removal using Generative Adversarial Networks

A pterostilbene derivative suppresses osteoclastogenesis by regulating RANKL-mediated NFκB and MAPK signaling in RAW264.7 cells.

An efficient Sign Language Recognition (SLR) system could facilitate communication with hearing impaired persons by identifying the sign gestures Similar to regional spoken languages, different regions have developed their own sign gesture representations (for example, American Sign Language (ASL), German Sign Language (GSL), Indian Sign Language (ISL), etc) Such variations in the hand shapes and movements add many challenges in the recognition process The overall SLR process can be divided into a number of modules such as hand and face detection, hand tracking, features extraction and gesture recognition In this paper, we propose a novel end-to-end SLR system from RGB video-sequences After detecting the skin color from video frames, Camshift tracker is employed to extract the trajectories of hand motion Next, Hidden Markov Model (HMM) based sequence classification is used to recognize the gestures A novel approach identifying double and single hand gestures is also proposed Furthermore new features, from skin region and hand trajectories, are proposed to improve the gesture classification performance We tested our system on dataset proposed by American Sign Language Linguistic Research Project (ASLLRP) [25], which consists of isolated signs The experiment results are encouraging

https://link.springer.com/content/pdf/10.1007/s42979-021-00485-z.pdf

An Efficient Sign Language Recognition (SLR) System Using Camshift Tracker and Hidden Markov Model (HMM)

Hormones mediate a major part of our essential physiological functions. Both endogenous and exogenous compounds and their metabolites are known to act through hormone receptors leading to regulation of endocrine function. The endogenous ligands that control reproductive functions are generally steroids such as 17beta-estradiol, androgens, progesterone, pregnenolone and glucocorticoids. However, exogenous compounds that are structurally and functionally similar gain entry into animals including humans through the diet or by occupational exposures, causing endocrine disruption. In the recent decade, there is a lot of apprehension about the so-called "endocrine disruptors" which are wide spread in the environment, mainly due to unrestricted human activity. These compounds of anthropogenic or natural origin mimic the action of sex hormones and can interfere with the endocrine system. It has been hypothesized that environmental exposure to synthetic estrogenic chemicals and related endocrine active compounds may be responsible for malformations in the male reproductive tract, crytorchidism, hypospadias, decrease in sperm counts, decreased male reproductive capacity and even testicular cancers. The increasing concern in both public and scientific communities about these abnormalities have prompted the initiation of epidemiological studies to not only identify, but to also analyze the short and long term effects of endocrine disruptors. As a result, a number of assays have been developed and are undergoing validation aimed at high throughput screening of chemical agents that disrupt endocrine activity. This review consolidates the findings of epidemiological studies, particularly in relation to male reproductive disorders and brings to light the various types of in vitro and in vivo models that are available for tiered testing of suspected compounds.

/pdf/a-treatise-on-hazards-of-endocrine-disruptors-and-tool-to-5yqp70hdrj.pdf

Partha Pratim Roy

Papers

Staff line Removal using Generative Adversarial Networks

A pterostilbene derivative suppresses osteoclastogenesis by regulating RANKL-mediated NFκB and MAPK signaling in RAW264.7 cells.

An Efficient Sign Language Recognition (SLR) System Using Camshift Tracker and Hidden Markov Model (HMM)

A treatise on hazards of endocrine disruptors and tool to evaluate them.

Word spotting in historical documents using primitive codebook and dynamic programming